Model-Based Compressive Sensing

@article{Baraniuk2010ModelBasedCS,
  title={Model-Based Compressive Sensing},
  author={Richard Baraniuk and V. Cevher and Marco F. Duarte and C. Hegde},
  journal={IEEE Transactions on Information Theory},
  year={2010},
  volume={56},
  pages={1982-2001}
}
  • Richard Baraniuk, V. Cevher, +1 author C. Hegde
  • Published 2010
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis. Instead of taking periodic samples, CS measures inner products with M < N random vectors and then recovers the signal via a sparsity-seeking optimization or greedy algorithm. Standard CS dictates that robust signal recovery is possible from M = O(K log(N/K)) measurements. It is possible to… CONTINUE READING
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